Integrating Graph Data Models in Advanced Water Resource Management: A New Paradigm for Complex Hydraulic Systems

The efficient management of water resources is crucial due to increasing environmental and societal pressures, requiring advanced data handling capabilities. Traditional relational data models, while foundational, struggle to capture the complex relationships inherent in water systems, limiting analytical and decision-making effectiveness. To address these challenges, this paper proposes a comprehensive graph data model for water resource management. Our model includes conceptual, logical, and physical frameworks that enable the structured representation of hydraulic objects and their relationships. We categorize hydraulic objects, examine their spatial, structural, and operational interconnections, and construct an adaptable graph data model. This model provides the enhanced integration, mining, and analysis of hydraulic data, supporting intelligent, data-driven water management applications. Example applications demonstrate the model’s utility in improving watershed management, hydropower scheduling, and uncovering latent relationships among hydraulic components through graph neural networks. This work establishes a robust data foundation for the sustainable and scientific management of water resources.